Unmanned Aerial Vehicle Attack Detection using Snort

Shahzad Mujeeb, Sunil Kumar Chowdhary, Abhishek Srivastava, Rana Majumdar, Manoj Kumar

2021

Abstract

In recent times, security issues relating to unmanned aerial vehicles (UAVs) and drones have anticipated a staid attention from research communities in various domains in the form of networking, communication, and civilian as well as in defence zone. It has its wide spread functionality in the area of agriculture, commerce, and transportation, the use of unmanned aerial vehicles (UAVs)/ drones, is increasing. The ground control systems (GCS) are used to remotely monitor UAVs over the network. Since UAVs are vulnerable to security risk, they become the targets of various attacks such as GPS spoofing, jamming attack, network attacks and many other forms so to tackle with such issues the prime concern will be to identify these attacks followed by to prevent the UAVs or drones from UAV attacks. On contrary network-controlled UAVs however are equally vulnerable to threats like DOS attacks, GPS spoofing etc. In this work a network surveillance approach is projected for UAV attack detection system by means of Snort. Snort uses a set of guidelines and rules set by the user itself to help in identifying the malicious network behaviour and to locate packets that fit them and create user warnings with those rules. It is an open source tool that records traffic analysis and packets in real time.

Paper Citation

in Harvard Style

Mujeeb S., Chowdhary S., Srivastava A., Majumdar R. and Kumar M. (2021). Unmanned Aerial Vehicle Attack Detection using Snort. In Proceedings of the 1st International Conference on Innovation in Computer and Information Science - Volume 1: ICICIS, ISBN 978-989-758-577-7, pages 18-24. DOI: 10.5220/0010789700003167

in Bibtex Style

@conference{icicis21,
author={Shahzad Mujeeb and Sunil Kumar Chowdhary and Abhishek Srivastava and Rana Majumdar and Manoj Kumar},
title={Unmanned Aerial Vehicle Attack Detection using Snort},
booktitle={Proceedings of the 1st International Conference on Innovation in Computer and Information Science - Volume 1: ICICIS,},
year={2021},
pages={18-24},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010789700003167},
isbn={978-989-758-577-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Innovation in Computer and Information Science - Volume 1: ICICIS,
TI - Unmanned Aerial Vehicle Attack Detection using Snort
SN - 978-989-758-577-7
AU - Mujeeb S.
AU - Chowdhary S.
AU - Srivastava A.
AU - Majumdar R.
AU - Kumar M.
PY - 2021
SP - 18
EP - 24
DO - 10.5220/0010789700003167